1. Field of the Invention
The present invention relates to a medical image processing device, a medical image processing method and a medical image processing program for extracting the endocardium of the left ventricle from 3D image data representing the left ventricle.
2. Description of the Related Art
Along with the spread of the multislice CT technology at medical treatment sites, medical image analyzing technologies are rapidly developing. For example, with respect to the heart region, it is now possible to obtain a plurality of 3D images during a single beat of the heart. With this development, functions of the heart, such as ejection rate, end-diastolic volume, end-systolic volume, single cardiac output, cardiac output and cardiac muscle weight, are analyzed by analyzing a plurality of pieces of 3D image data obtained by imaging the heart.
In order to achieve such analysis of the cardiac functions based on image information, it is necessary to accurately extract the position of the endocardium of the left ventricle from 3D image data representing the left ventricle. As a method for extracting the endocardium from the image data, a technique called Active Shape Model has been proposed, wherein a standard model of the endocardium of the left ventricle is deformed to conform to an image of interest in order to find the endocardium, as disclosed, for example, in Y. Zheng et al., “Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features”, IEEE Transactions on Medical Imaging, Vol. 27, No. 11, pp. 1668-1681, 2008 (hereinafter, Non-patent Document 1).
In this technique, the standard model of the shape of the endocardium is generated in advance through learning the shape of the endocardium using a plurality of sample images. The standard model is represented by an average value and a principal component of the shape at each position. Then, the average shape of the generated standard model is placed on an image of interest, from which the shape of the endocardium is to be extracted. For each feature point on the average shape of the standard model placed on the image of interest, a feature quantity relative to a direction perpendicular to the shape is calculated and is compared with feature quantities which have been calculated during the learning, and an amount of shift, by which each feature point is to be moved to bring the feature point to a location where the best match is found between the feature quantities, is calculated. Then, a principal component factor which can best express the amount of shift calculated for each feature point is found, and the shape of the standard model is deformed using the thus found principal component factor. Further, for the deformed standard model, the calculation of the amount of shift and the deformation based on the result of the calculation are repeated until the shape is converged (until a predetermined energy function is minimized). Then, a finally obtained deformed standard model is obtained as the endocardium in the image of interest.
With the above-described conventional method, however, the shape of the endocardium to be extracted is limited by the standard model. Therefore, although the result of the extraction of the endocardium does not deviate from a proper shape of the endocardium, the endocardium in the image of interest may not accurately be extracted correspondingly to variation of the shape due to individual differences. For example, in a case where a convex portion or concave portion is locally present in the endocardium, some standard model cannot express such a shape of the endocardium in the image of interest, and even when an amount of shift for each feature point is correctly calculated, the shape may converge at a local solution (which is not an optimal solution), resulting in unsuccessful acquisition of a desired shape of the endocardium.